Neurology News

The likelihood of acquiring Alzheimer’s disease in the future may be predicted using a mix of genetics and brain MRIs, according to research being done in this area.

In a recently released study, scientists from SFU’s Functional and Anatomical Imaging & Shape Analysis Lab (FAISAL) identified specific traits of brain MRIs and genetics that affect the prediction of Dementia of Alzheimer’s Type, or DAT, for patients at different stages of the disease, and then developed a biomarker that can aid in predicting future conversion to DAT.

“Our findings reveal that while genetic features have lower predictive power than MRI features, combining both modalities can improve the performance in predicting the future conversion to DAT,” says study lead author Ghazal Mirabnahrazam, a research assistant currently completing a master’s degree in engineering science at SFU.

Dementia scores based on genetic data were shown to better predict future DAT progression in currently normal patients who will develop DAT at a later time, while MRI data, which reflects anatomical changes in the brain, was shown to better predict future DAT in those with mild cognitive impairment.

“In a clinical setting, clinicians can use our model to predict a quantitative score indicating the similarity between a subject’s observed patterns based on MRI and genetic data at the time of clinical visit and DAT patterns,” says senior author Mirza Faisal Beg, a professor in SFU’s School of Engineering Science.

“This is extremely useful, specifically at the MCI (mild cognitively impaired) stage in identifying those who will progress to DAT in the future. Being able to accurately estimate the chance of future conversion to DAT using only baseline information is extremely valuable because it provides practitioners with deep insight and enough time to plan appropriate care for each patient based on their probability of developing Alzheimer’s disease.

“Furthermore, it can provide potentially critical information for drug trials and the development of preventative measures,” adds Beg. “This information can aid in the selection of the appropriate cohort of patients for clinical trials, which can lead to a more promising outcome.”

The recent research from the University of East Anglia (UK) has revealed that cranberries have neuroprotective properties and that include them in one’s diet can dramatically improve memory and lower harmful cholesterol.

The study looked at the health advantages of eating a cup of cranberries per day in people aged 50 to 80. They anticipate that their results would help in the prevention of neurodegenerative illnesses like dementia.

“Dementia is anticipated to afflict about 152 million individuals by 2050,” stated lead researcher Dr David Vauzour of UEA’s Norwich Medical School.

Because there is no known treatment, it is critical that we pursue modifiable lifestyle measures, such as food, to reduce disease risk and burden.

“Previous research has linked increased dietary flavonoid consumption to slower rates of cognitive deterioration and dementia. Furthermore, diets high in anthocyanins and proanthocyanidins, which give berries their red, blue, or purple colour, have been shown to benefit cognition.

“Cranberries are high in these micronutrients and have been linked to anti-inflammatory and antioxidant capabilities.

“We wanted to learn more about how cranberries may aid in the prevention of age-related neurodegeneration.”

The study looked at how consuming cranberries for 12 weeks affected brain function and cholesterol in 60 cognitively healthy people.

Half of the participants ingested freeze-dried cranberry powder on a daily basis, which was comparable to a cup or 100g of fresh cranberries. The other half was given a placebo.

This is one of the first studies to look into cranberries and their long-term effects on cognition and brain health in people.

Cranberries dramatically enhanced participants’ recollection of ordinary experiences (visual episodic memory), neuronal functioning, and blood transport to the brain, according to the findings (brain perfusion).

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Dr. Vauzour stated: “Participants who consumed the cranberry powder demonstrated significantly improved episodic memory performance, as well as improved circulation of essential nutrients such as oxygen and glucose to important parts of the brain that support cognition – specifically memory consolidation and retrieval.

“The cranberry group also showed a substantial reduction in LDL or ‘bad’ cholesterol levels, which are known to contribute to atherosclerosis, which is the thickening or hardening of the arteries caused by a buildup of plaque in the inner lining of an artery.”

This lends credence to the notion that cranberries might enhance vascular health and, as a result, may contribute to improvements in brain perfusion and cognition.

“Demonstrating in people that cranberry supplementation may improve cognitive function and understanding some of the processes involved is a critical step in this study field.”

“The outcomes of this study are really encouraging,” he noted, “particularly given that a very brief 12-week cranberry intervention was able to elicit considerable changes in memory and brain function.”

“This lays a solid platform for future study into cranberries and brain health.”

The Cranberry Institute provided funding for the study. It was directed by the University of East Anglia in partnership with academics from the Netherlands’ Leiden University Medical Center, Italy’s University of Parma, and the Quadram Institute (UK).

Andrea Benucci and colleagues at the RIKEN Center for Brain Science have built artificial neural networks that learn to detect things more rapidly and precisely.

The study, which was just published in the scientific journal PLOS Computational Biology, focuses on all of our unconscious eye movements. It demonstrates that they play an important role in helping us to recognise items consistently.

These discoveries may be used to machine vision, for example, to help self-driving cars learn how to distinguish crucial road elements.

Objects in the environment do not blur or become unidentifiable despite our frequent head and eye movements throughout the day, despite the fact that the actual information striking our retinas changes continually.

This perceived constancy is most likely enabled by brain copies of movement commands. These copies are supposed to be transmitted throughout the brain each time we move, allowing the brain to account for our own motions and maintain our perception steady.

In addition to steady perception, research shows that eye movements and associated motor duplicates may assist humans in recognising things in the world, albeit how this occurs is unknown.

Benucci created a convolutional neural network (CNN) that solves this challenge. The CNN was created to maximise item categorization in a visual scene while the eyes are moving.

First, the network was taught to categorise 60,000 black-and-white photos into ten groups. Although it performed well on these pictures, when tested with shifted images that mirrored naturally changing visual input that would occur as the eyes moved, performance plummeted to the level of chance.

However, categorization improved dramatically after training the network using altered pictures, as long as the direction and magnitude of the resulting eye movements were also incorporated.

In particular, adding the eye movements and their motor copies to the network model allowed the system to better cope with visual noise in the images. “This advancement will help avoid dangerous mistakes in machine vision,” said Benucci. “With more efficient and robust machine vision, it is less likely that pixel alterations–also known as ‘adversarial attacks’–will cause, for example, self-driving cars to label a stop sign as a light pole, or military drones to misclassify a hospital building as an enemy target.”

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Bringing these results to real-world machine vision is not as difficult as it seems. As Benucci explains, “the benefits of mimicking eye movements and their efferent copies implies that ‘forcing’ a machine-vision sensor to have controlled types of movements, while informing the vision network in charge of processing the associated images about the self-generated movements, would make machine vision more robust, and akin to what is experienced in human vision.”

The next step in this research will involve collaboration with colleagues working with neuromorphic technologies. The idea is to implement actual silicon-based circuits based on the principles highlighted in this study and test whether they improve machine-vision capabilities in real-world applications.

Individuals who have at least two of the conditions, such as type 2 diabetes, heart disease, or stroke, are twice as likely to acquire dementia.

Diabetes and cardiovascular disease prevention might thus be a strategy for lowering dementia risk, according to a study published in the journal Alzheimer’s & Dementia by Karolinska Institutet in Sweden. Type 2 diabetes, heart disease (ischemic heart disease, heart failure, or atrial fibrillation), and stroke, all of which are known as cardiometabolic illnesses, are major risk factors for dementia.

“Few studies have examined how the risk of dementia is affected by having more than one of these diseases simultaneously, so that’s what we wanted to examine in our study,” ANI quoted Abigail Dove, a doctoral student at the Aging Research Centre, part of the Department of Neurobiology, Care Sciences and Society, Karolinska Institutet.

Dementia develops gradually over time. It begins with slow cognitive impairment that is only visible on cognitive tests. It then progresses to cognitive impairment, in which the individual acknowledges their diminishing memory but is still able to care for oneself, and lastly to dementia.

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More than one cardiometabolic disease doubles the dementia risk

The researchers gathered information from the Swedish National Study on Aging and Care on 2,500 healthy, dementia-free people over the age of 60 who lived on Kungsholmen in Stockholm. The prevalence of cardiometabolic illnesses was determined at the outset of the research using medical records and clinical examination. After that, the subjects were followed for twelve years with medical exams and cognitive testing to track changes in cognitive capacity and the development of dementia.

The presence of several cardiometabolic diseases accelerated cognitive decline and increased the likelihood of cognitive impairment and dementia, accelerating their development by two years. The degree of the danger grew as the number of illnesses increased.

“In our study, the combinations of diabetes/heart disease and diabetes/heart disease/stroke were the most damaging to cognitive function,” says Dove.

Prevention of a second disease important

However, individuals who had just one cardiometabolic disease did not display a significantly higher risk of dementia.
“This is good news. The study shows that the risk only increases once someone has at least two of the diseases, so it’s possible that dementia can be averted by preventing the development of a second disease.”

The correlation between cardiometabolic diseases and the risk for dementia was stronger in the participants who were under 78 years old.

“We should therefore focus on cardiometabolic disease prevention already in middle age since the risk of cognitive failure and dementia appears higher among those who develop a cardiometabolic disease earlier in life,” says Dove.

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According to the findings of a study conducted by the Barcelona Institute for Global Health (ISGlobal), more exposure to air pollution is related with stronger functional brain connectivity across numerous brain areas in preadolescents, although exposure to traffic noise is not.

The findings also identify the first years of life as the most sensitive period of exposure to air pollution. Traffic-related air pollution and noise are affecting an increasing number of people worldwide. “We already know that children are particularly vulnerable to the effect of these exposures, because of their immature metabolism and developing brain,” says ISGlobal researcher and senior author Monica Guxens. In fact, several studies by Guxens and others have found an association between exposure to traffic-related air pollution during early childhood and alterations in the brain structure.

In this study, the research team used magnetic resonance imaging (MRI) to explore whether higher exposure to air pollution or noise could also be associated with possible alterations in brain connectivity (i.e. the way in which different brain regions interact). “The use of MRI has opened up new possibilities in epidemiological research for investigating the structure and the functioning of the brain,” says Guxens.

The researchers analysed data from the Generation R Study, which included 2,197 infants born between April 2002 and January 2006 in Rotterdam, the Netherlands. They used land use models to estimate levels of nitrogen oxides (NOx and NO2) and particulate matter (PM) in the participants’ residences at various times: during pregnancy, from birth to 3 years, from 3 to 6 years, and from 6 years to the age of the MRI scan. Traffic noise levels were assessed using available noise maps. Participants between the ages of 9 and 12 were asked to undertake a resting MRI scan (i.e. with no external stimuli).

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The findings show that higher exposures to NO2 and PM2.5 absorbance (an indicator of black carbon particles) from birth to 3 years, and to NOx from 3 to 6 years of age were associated with higher functional brain connectivity among several brain regions in the preadolescents. The associations were identified in brain areas predominantly involved in two networks that have strongly opposing functions: the task negative (or “default-mode”) network tends to be activated in resting conditions and the task positive network tends to be activated during tasks that demand attention. “We still have to understand the consequences of this increased activity of both networks in resting conditions, but for now we can say that the brain connectivity in children exposed to higher levels of air pollution is different from what we would expect,” says Laura Perez-Crespo, first author of the study.

0-3 year olds most vulnerable to air pollution

The period from birth to three years had the most vulnerability to air pollution, and black carbon was the pollutant most related with alterations in brain connections. As the authors point out, diesel cars are the primary source of black carbon and nitrogen oxide pollutants in European cities. Despite the fact that multiple studies have shown that noise impacts cognitive development in children, noise exposure at home was not connected with variations in brain connection.

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